class_docstr: Abstract object representing a fused RNN cell.
  
  A fused RNN cell represents the entire RNN expanded over the time
  dimension. In effect, this represents an entire recurrent network.
  
  Unlike RNN cells which are subclasses of `rnn_cell.RNNCell`, a `FusedRNNCell`
  operates on the entire time sequence at once, by putting the loop over time
  inside the cell. This usually leads to much more efficient, but more complex
  and less flexible implementations.
  
  Every `FusedRNNCell` must implement `__call__` with the following signature.
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docstr: Run this fused RNN on inputs, starting from the given state.
  
  Args:
    inputs: `3-D` tensor with shape `[time_len x batch_size x input_size]`
      or a list of `time_len` tensors of shape `[batch_size x input_size]`.
    initial_state: either a tensor with shape `[batch_size x state_size]`
      or a tuple with shapes `[batch_size x s] for s in state_size`, if the
      cell takes tuples. If this is not provided, the cell is expected to
      create a zero initial state of type `dtype`.
    dtype: The data type for the initial state and expected output. Required
      if `initial_state` is not provided or RNN state has a heterogeneous
        dtype.
    sequence_length: Specifies the length of each sequence in inputs. An
      `int32` or `int64` vector (tensor) size `[batch_size]`, values in `[0,
      time_len)`.
      Defaults to `time_len` for each element.
    scope: `VariableScope` or `string` for the created subgraph; defaults to
      class name.
  
  Returns:
    A pair containing:
  
    - Output: A `3-D` tensor of shape `[time_len x batch_size x output_size]`
      or a list of `time_len` tensors of shape `[batch_size x output_size]`,
      to match the type of the `inputs`.
    - Final state: Either a single `2-D` tensor, or a tuple of tensors
      matching the arity and shapes of `initial_state`.
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code: def __call__(
      self,
      inputs,
      initial_state=None,
      dtype=None,
      sequence_length=None,
      scope=None,
  ):
      """Run this fused RNN on inputs, starting from the given state.
  
      Args:
        inputs: `3-D` tensor with shape `[time_len x batch_size x input_size]`
          or a list of `time_len` tensors of shape `[batch_size x input_size]`.
        initial_state: either a tensor with shape `[batch_size x state_size]`
          or a tuple with shapes `[batch_size x s] for s in state_size`, if the
          cell takes tuples. If this is not provided, the cell is expected to
          create a zero initial state of type `dtype`.
        dtype: The data type for the initial state and expected output. Required
          if `initial_state` is not provided or RNN state has a heterogeneous
            dtype.
        sequence_length: Specifies the length of each sequence in inputs. An
          `int32` or `int64` vector (tensor) size `[batch_size]`, values in `[0,
          time_len)`.
          Defaults to `time_len` for each element.
        scope: `VariableScope` or `string` for the created subgraph; defaults to
          class name.
  
      Returns:
        A pair containing:
  
        - Output: A `3-D` tensor of shape `[time_len x batch_size x output_size]`
          or a list of `time_len` tensors of shape `[batch_size x output_size]`,
          to match the type of the `inputs`.
        - Final state: Either a single `2-D` tensor, or a tuple of tensors
          matching the arity and shapes of `initial_state`.
      """
      pass
  
file_path: spark-nlp/python/sparknlp/training/_tf_graph_builders/tf2contrib/fused_rnn_cell.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import six
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import rnn
label_desc: Use image node_id 1 for calling the FusedRNNCell obj's underlying member method code with example usage: obj.__call__(inputs, initial_state, dtype, sequence_length, scope) without return types